Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study

In this paper, joint modeling of longitudinal ordinal measurements and time to some events of interest as competing risks is discussed. For this purpose, a latent variable sub-model under linear mixed-effects assumption is considered for modeling ordinal longitudinal measurements. Also, a Weibull cause-specific sub-model is used to model competing risks data. These two sub-models are simultaneously considered in a unique model by a shared parameter model framework. Some simulation studies are performed for illustration of the proposed approaches; also, the proposed approaches are used for analyzing 15 years of lipid and glucose follow-up study in Tehran.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:30

Enthalten in:

Journal of biopharmaceutical statistics - 30(2020), 4 vom: 03. Juli, Seite 689-703

Sprache:

Englisch

Beteiligte Personen:

Baghfalaki, Taban [VerfasserIn]
Kalantari, Shiva [VerfasserIn]
Ganjali, Mojtaba [VerfasserIn]
Hadaegh, Farzad [VerfasserIn]
Pahlavanzadeh, Bagher [VerfasserIn]

Links:

Volltext

Themen:

Bayesian paradigm
Biomarkers
Blood Glucose
Competing risks
Joint modeling
Journal Article
Latent variable
Linear mixed model
Lipids
Longitudinal data
Ordinal data

Anmerkungen:

Date Completed 02.08.2021

Date Revised 02.08.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1080/10543406.2020.1730876

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM307210154